Mohsen Fatehifar, Kevin J Munro, Michael A Stone, David Wong, Tim Cootes, Josef Schlittenlacher
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The study involved 31 participants: 10 with hearing loss and 21 with normal-hearing. Achieved mean bias and limits-of-agreement showed that the agreement between the AI-powered test and the independent test (-1.3 ± 4.9 dB) was similar to the agreement between the keyboard-based test and the Independent test (-0.2 ± 4.4 dB), indicating that the addition of TTS and ASR did not have a negative impact. The AI-powered test had a reliability of -1.0 ± 5.7 dB, which was poorer than the baseline reliability (-0.4 ± 3.8 dB), but this was improved to -0.9 ± 3.8 dB when outliers were removed, showing that low-error ASR (as shown with the Whisper model) makes the test as reliable as independent tests. These findings suggest that a digits-in-noise test using synthetic stimuli and automatic speech recognition is a viable alternative to traditional tests and could have real-world applications.</p>","PeriodicalId":48678,"journal":{"name":"Trends in Hearing","volume":"29 ","pages":"23312165251367625"},"PeriodicalIF":3.0000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12489207/pdf/","citationCount":"0","resultStr":"{\"title\":\"Digits-In-Noise Hearing Test Using Text-to-Speech and Automatic Speech Recognition: Proof-of-Concept Study.\",\"authors\":\"Mohsen Fatehifar, Kevin J Munro, Michael A Stone, David Wong, Tim Cootes, Josef Schlittenlacher\",\"doi\":\"10.1177/23312165251367625\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>This proof-of-concept study evaluated the implementation of a digits-in-noise test we call the 'AI-powered test' that used text-to-speech (TTS) and automatic speech recognition (ASR). Two other digits-in-noise tests formed the baselines for comparison: the 'keyboard-based test' which used the same configurations as the AI-powered test, and the 'independent test', a third-party-sourced test not modified by us. The validity of the AI-powered test was evaluated by measuring its difference from the independent test and comparing it with the baseline, which was the difference between the Keyboard-based test and the Independent test. The reliability of the AI-powered test was measured by comparing the similarity of two runs of this test and the Independent test. The study involved 31 participants: 10 with hearing loss and 21 with normal-hearing. Achieved mean bias and limits-of-agreement showed that the agreement between the AI-powered test and the independent test (-1.3 ± 4.9 dB) was similar to the agreement between the keyboard-based test and the Independent test (-0.2 ± 4.4 dB), indicating that the addition of TTS and ASR did not have a negative impact. 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Digits-In-Noise Hearing Test Using Text-to-Speech and Automatic Speech Recognition: Proof-of-Concept Study.
This proof-of-concept study evaluated the implementation of a digits-in-noise test we call the 'AI-powered test' that used text-to-speech (TTS) and automatic speech recognition (ASR). Two other digits-in-noise tests formed the baselines for comparison: the 'keyboard-based test' which used the same configurations as the AI-powered test, and the 'independent test', a third-party-sourced test not modified by us. The validity of the AI-powered test was evaluated by measuring its difference from the independent test and comparing it with the baseline, which was the difference between the Keyboard-based test and the Independent test. The reliability of the AI-powered test was measured by comparing the similarity of two runs of this test and the Independent test. The study involved 31 participants: 10 with hearing loss and 21 with normal-hearing. Achieved mean bias and limits-of-agreement showed that the agreement between the AI-powered test and the independent test (-1.3 ± 4.9 dB) was similar to the agreement between the keyboard-based test and the Independent test (-0.2 ± 4.4 dB), indicating that the addition of TTS and ASR did not have a negative impact. The AI-powered test had a reliability of -1.0 ± 5.7 dB, which was poorer than the baseline reliability (-0.4 ± 3.8 dB), but this was improved to -0.9 ± 3.8 dB when outliers were removed, showing that low-error ASR (as shown with the Whisper model) makes the test as reliable as independent tests. These findings suggest that a digits-in-noise test using synthetic stimuli and automatic speech recognition is a viable alternative to traditional tests and could have real-world applications.
Trends in HearingAUDIOLOGY & SPEECH-LANGUAGE PATHOLOGYOTORH-OTORHINOLARYNGOLOGY
CiteScore
4.50
自引率
11.10%
发文量
44
审稿时长
12 weeks
期刊介绍:
Trends in Hearing is an open access journal completely dedicated to publishing original research and reviews focusing on human hearing, hearing loss, hearing aids, auditory implants, and aural rehabilitation. Under its former name, Trends in Amplification, the journal established itself as a forum for concise explorations of all areas of translational hearing research by leaders in the field. Trends in Hearing has now expanded its focus to include original research articles, with the goal of becoming the premier venue for research related to human hearing and hearing loss.